Pitch perception models - a historical review
Alain de Cheveign´e
CNRS - Ircam,
This paper analyzes theories and models of pitch from
a historical perspective. Pythagoras is credited with
the first ”psychophysical” model, the monochord, that
he used to formulate a law that links a physical quantity
(ratio of string lengths) to a psychological quantity
(musical interval). The relation between pitch itself
and frequency emerged progressively with Aristoxenos,
Boethius, Mersenne and Galileo. The anatomist Du Verney
first proposed the idea of resonance within the ear,
and of a ”tonotopic” projection from the ear to the brain.
The notion of frequency analysis, formalized mathematically
by Joseph Fourier, was developed by Helmholtz into
a beautiful theory of hearing that bridged mathematics,
physiology and music. Helmholtz unfortunately followed
Ohmin postulating that pitch is determined by one particular
frequency component, the fundamental, thus sparking
a controversy that has drained energy of hearing scientists
for decades, opposing tenants of ”spectral pitch”
to tenants of ”temporal pitch”. Today the terms of the
disagreement have shifted, and the disagreement is now
between models based on ”pattern matching” (originated
by de Boer but already hinted to by Helmholtz) and those
based on ”autocorrelation” (originated by Licklider, but
already implicit in earlier work). Despite the disagreements,
there are deep connections between these various
theories of pitch, and between them and the many methods
that have been proposed for the artificial equivalent
of pitch perception: fundamental frequency estimation.
Using a historical perspective I will try to make apparent
these relations between models and methods. The aim
is to help us go beyond the controversies and develop a
better understanding of how we perceive pitch.
The history of yesterday’s ideas suggests that today’s
might not last, and that better ones await us in the future.
By looking carefully at theories that did not survive, we
may learn to identify the weak points of our own theories
and fix them. The historical perspective has other virtues.
Among factors that slow down progress in Science, Boring
cites the need to conform to the “Zeitgeist”, the spirit
of the times . Another factor is controversy that may
lock progress into sterile argument. History serves as
an antidote to these factors. Models are often reincarnations
of older ideas, themselves with roots deeper in
time. By digging up the roots we can see the commonalities
and differences between successive or competing
models. Anyone who likes ideas will find many good
ones in the history of science.
Some early theories focused on explaining consonance
and musical scales , others on the physiology
of the ear , and others again on the physics of sound
[4, 5, 6]. Certain thinkers, such as Helmholtz , have
tried to address all these aspects, others were less ambitious.
Music once constituted a major part of Science,
and theories of music were theories of the world. Today,
music and science go each their own way, and the goal
of hearing science is more modestly to explain how we
perceive sound. However, music is still an important part
of our auditory experience, and, historically, theories of
hearing have often been theories of musical pitch.
Today, two competing explanations of pitch prevail:
autocorrelation and pattern-matching, that inherit from
the rival theories of place and time, themselves rooted in
early concepts of resonance and time interval. Autocorrelation
and pattern-matching each have variants. The historical
perspective reveals both their unity and the originalities
of each, and suggests directions in which future
models might evolve. This paper is a short version of an
upcoming chapter on pitch perception models .
2.1. Interval and ratio, pitch and frequency
Pythagoras (6th century BC) is credited for relating musical
intervals to ratios of string length on a monochord
. The monochord consists of a board with two bridges
between which a string is stretched. A third bridge divides
the string in two parts. Intervals of unison, octave,
fifth and fourth arise for length ratios of 1:1, 1:2, 2:3,
3:4, respectively. The monochord can be seen as an early
example of a psychophysical model, in that it relates the
perceptual property of musical interval to a ratio of physical
quantities. The physics of the model were quickly
occluded by the mathematics or mystics of the numbers
involved in the ratios . Ratios of numbers between 1
and 4 were taken to govern both musical consonance and
the relations between heavenly bodies. Aristoxenos (4th
century BC) disagreed with the Pythagoreans that numbers
are relevant to music, and instead argued that musical
scales should be defined based on what one hears .
Two millenia later, Descartes made the same objection to
Mersenne ). In 1581 the role of number was also challenged,
from a different perspective, by Vincenzo Galilei
(father of Galileo). Using weights to vary the tension of
a string, he found that the abovementioned intervals arise
for ratios of 1:1, 1:4, 4:9, and respectively [10, 2].
These ratios are different from those found for length:
they are more complex, and don’t agree with the importance
that the Pythagoreans gave to numbers from 1 to
4. Deciding the respective roles of mathematics, physics,
and perception in the “laws” of music is still a problem
In addition to interval, the Greeks had the concept of
pitch, a quantity by which sounds can be ordered from
grave to acute . They probably associated it with rate,
but semantic overlaps between rate (of vibration), speed
(of propagation) and force (of excitation) makes this unsure.
The relation between ratios of string length and
ratios of vibration frequency was established by Galileo
Galilei , whereas Mersenne , using strings long
enough to count vibrations, determined the actual frequencies
of each note of the scale. This provided a relation
of pitch with number that was firmly grounded in
the physics of sound.
2.2. Sympathetic resonance in the ear
A string produces musical sounds, but it can also vibrate
in sympathetic resonance as noted by Aristotle . The
perception of like by like was a common notion, and so
the concept of resonance has been used in theories of
hearing from antiquity onwards [1, 3, 6].
In 1683, Du Verney proposed that the bony spiral
lamina within the cochlea serves as a resonator:
“. . . being wider at the start of the first turn than the end
of the last . . . the wider parts can be caused to vibrate
while the others do not . . . they are capable of slower vibrations
and consequently respond to deeper tones . . . in
the same way as the wider parts of a steel spring vibrate
slowly and respond to low tones, and the narrower parts
make more frequent and faster vibrations and respond to
sharp tones . . . according to the various motions of the
spiral lamina, the spirits of the nerve which impregnate
its substance [that of the lamina] receive different impressions
that represent within the brain the various aspects
of tones” . This paragraph concentrates several key
concepts of place theory: frequency-selective response,
tonotopy, and tonotopic projection to the brain. Subsequent
progress of the resonance theory is recounted in
. In 1758, Le Cat  proposed that the basilar membrane
is constituted of strings like those of a harpsichord,
and Helmholtz later used a similar metaphor.
2.3. Superposition and Ohm’s law
Mersenne reported that he could hear within the sound of
a string, or a voice, up to five pitches corresponding to the
fundamental, the octave, the octave plus fifth, etc. .
He knew also that a string can respond sympathetically
to higher harmonics, and yet he found it hard to accept
that it could vibrate simultaneously at all those frequencies.
This was easier for a younger mind such as that
of Sauveur, who in 1701 coined the terms “fundamental”
and “harmonic” . The physics of string vibration
were worked out in the 18th century by a succession of
physicists: Taylor, Daniel Bernoulli, Lagrange, dAlembert,
and Euler . Euler in particular, by introducing
the concept of linear superposition, made it easy to understand
the multiple vibrations of a string that had so
Mersenne and Galileo usually conceived of vibrations
as merely being periodic, without regard to their shape,
but 18th century physicists found that solutions were often
easy to derive if they assumed “pendular” (sinusoidal)
vibrations. For linear systems, they could then extend the
solutions to any sum of sinusoids thanks to Euler’s principle.
A wide variety of shapes can be obtained in this
way, meaning that the method was quite general. That
any shape can be obtained in this way was proved in
1820 by Fourier . In particular, any periodicwave can
be expressed as the superposition of sinusoids with periods
that are integer fractions of the fundamental period.
Fourier’s theoremhad a tremendous impact onmathematics
Up to that point, pitch had been closely associated
with progress in the physics of periodic vibration, and
it seemed obvious that this new tool must somehow be
relevant to pitch. In 1843 Ohm formulated a law, later
rephrased and clarified by Helmholtz, according to which
every pitch corresponds to a sinusoidal partial within the
stimulus waveform. For Ohm, the presence of a partial
was ascertained by applying Fourier’s theorem, and
Helmholtz proposed that the same operation is approximated
by the cochlea[17, 7].
Ohm’s law extended the principle of linear superposition
to the sensory domain. Just as a complex waveform
is the sum of sinusoids, so for Helmholtz the sensation
produced by a complex sound such as a musical note was
“composed” of simple sensations, each evoked by a partial.
In particular, he associated the main pitch of a musical
tone to its fundamental partial.
2.4. The missing fundamental
Ohm’s law is the result of a choice. Mersenne had given
little attention to the shape of periodic vibrations which
he had no means to observe. His law relating frequency
to pitch did not mention shape. However, Fourier’s theorem
now implied that, depending on its shape, a vibration
might contain several sinusoidal partials, each with a different
frequency. This raised an obvious question: does
pitch relate (a) to the period of the vibration as a whole,
or (b) to the period of one of the partials? If (b) is true,
then Fourier analysis is required to determine pitch, if (a)
it is unnecessary. Ohm chose (b).
Seebeck had already addressed the question experimentally,
using a siren to produce periodic stimuli with
several pulses irregularly spaced within a period. Regardless
of the number of pulses, pitch followed the fundamental
period, consistent with (a). Furthermore, by
applying Fourier’s theorem to the waveform, Seebeck
showed that pitch salience did not depend on the relative
amplitude of the fundamental partial, which for some
pulse configurationswas very small. Since the same pitch
was also heard when the stimulus contained only that partial,
he could conclude that pitch does not depend on a
particular partial. This contradicted (b). Low pitch in
the absence of a fundamental partial was already known
from earlier work on beats .
Nevertheless, Ohm chose (b) and Helmholtz endorsed
this choice. Many authors have puzzled over
the Seebeck-Ohm-Helmholtz controversy and the reasons
why Helmholtz did not take seriously Seebeck’s
arguments[1, 19, 20, 21]. One reason was no doubt that,
by extending Ohm’s law to upper harmonics, Helmholtz
could explain the higher pitches that some people (among
which Mersenne and himself) occasionally heared. One
can speculate that additional reasons were the conviction
that a theorem as powerful as Fourier’s must be relevant,
and the desire to ensure that the parts of his monumental
theory would fit together.
Helmholtz had three options to address the missing
fundamental problem without renouncing his theory, two
of which he used. The first was to invoke nonlinear distortion
in sound-producing apparatus or in the ear. As an
explanation of periodicity pitch, that hypothesiswas quite
weak already at the time, as argued by Helmholtz’s translator,
Ellis . However it took over sixty years before
Schouten and Licklider laid the explanation to rest. With
an optical siren, Schouten produced a complex tone that
lacked a fundamental. He managed, not only to prove
that the distortion product at the fundamental had a very
low amplitude, but also to cancel it. The absence of a fundamental
component was verified by adding a sinusoidal
tone with a nearby frequency and checking for absence of
a beat. The low pitch was unaffected by removeal of the
fundamental partial, as it was unaffected when Licklider
masked it with noise [22, 23]. This rules out the distortion
product explanation of lowpitch. However distortion
products do exist, and they sometimes do affect pitch, so
that explanation tends to resurface from time to time.
A second optionwas Helmholtz’s concept of “unconscious
inference” that prefigured pattern matching (next
section). A third option, that Helmholtz apparently
did not use, was to treat cochlear resonators as strings.
As Mersenne and others had noticed, a string vibrates
sympathetically with sounds tuned to its fundamental
mode and with their harmonics. Thus it responds
to a periodic sound regardless of whether or not it contains
a fundamental partial. It is, in essence, a filter tuned
to periodicity. Helmholtz had used the bank-of-strings
metaphor to describe the cochlea. Nevertheless, he chose
to characterize each filter as if it were a Helmholtz resonator
tuned to a single sinusoidal partial. Had he chosen
to treat them as a strings, the missing fundamental
problem would have not existed. Of course, a bank of
strings does not fit Fourier’s theorem, and this is perhaps
why he did not choose this option. If he had chosen it,
the model would have eventually been proven wrong as
cochlear filters are not tuned to periodicity.
3. Pattern matching
We are confronted with incomplete patterns everyday,
and our brain is good at “reconstructing” perceptually the
parts that are missing. Pattern matching models assume
that this is how pitch is perceived when the fundamental
partial is missing. The idea is thus that the fundamental
partial is the necessary correlate of pitch, as Ohm
claimed, but that it may nevertheless be absent if other
parts of the pattern (harmonics often associated with it)
are present. This idea was prefigured byHelmholtz’s “unconscious
inference” and John Stuart Mill’s concept of
“possibilities” [24, 1]. As a possible mechanism, Thurlow
suggested that listeners use their own voice as a
“template” to match with incoming patterns of harmonics
In 1956, de Boer described the concept of pattern
matching in his thesis , but the best-known models
are those of Goldstein , Wightman  and Terhardt
. These models are closely related, but each has its
characteristic flavor. Goldstein’s is probabilistic and performs
optimum processing of a set of estimates of partial
frequencies (obtained by a process that is not defined, but
that could be Helmholtz’s cochlear analysis). Wightman
takes the limited-resolution profile of activity across the
cochlea, and feeds it to a hypothetical internal “Fourier
transformer” to obtain a pattern akin to the autocorrelation
function. Terhardt follows Ohm in positing for each
partial its own sensation of spectral pitch, from which an
internal template derives a virtual pitch that matches that
of the (possibly missing) fundamental. That template is
Pattern-matching models are well known and will not
be described in greater detail here. There is a close relationship
between pattern-matching models and spectrumbased
signal-processing methods for fundamental frequency
estimation, such as subharmonic summation, harmonic
sieve, autocorrelation or cepstrum [30, 8]. For the
last two, this reflects the fact that the Fourier transform,
applied to a spectrum (power spectrum for autocorrelation,
log spectrum for cepstrum) is sensitive to the regular
pattern of harmonics.
Terhardt’smodel is distinct in that it requires that templates
be learned by exposure to harmonically rich stimuli,
an idea that is attractive but constraining. It can be
argued that learning could just as well occur from exposure
to patterns of subharmonics (superperiods) of periodic
sounds, and that harmonically rich stimuli are thus
unnecessary . Shamma and Klein went further and
showed that templates may be learned by exposure to
noise . What this suggests is that the harmonic relations
within the template are a mathematical property
that needs merely to be discovered, not learned. Indeed,
other devices embody the pattern-matching properties of
a harmonic template without having “learned” them. Examples
are the autocorrelation function and the string.
4. Temporal models
Democritus (5th century BC) and Epicurus (4th century
BC) are credited with the idea that a sound-producing
body emits atoms that propagate to the listener’s ear, an
idea later adopted by Beeckman and Gassendi . Related
is the idea that a string “hits” the air repeatedly, and
that pitch reflects the rate at which sound pulses hit the ear
[32, 2]. If so, it should be a simple matter to measure the
interval between two consecutive atoms or pulses, rather
than wait for a series of pulses to build up sympathetic
vibration in a resonator.
The influence of this temporal view of pitch can be
observed indirectly in the “coincidence” theories of consonance
that developed in the 16th and 17th centuries.
Two notes were judged consonant if their vibrations coincided
Early temporal models assumed that patterns of
pulses are handled by the “brain”, and thus they tend to
be less elaborate than resonance models. Compare for
example Anaxagoras (5th century BC) for whom hearing
involved penetration of sound to the brain, and Alcmaeon
is by means of the ears, because within them is an empty
space, and this empty space resounds . The latter obviously
“explains” more. A similar contrast is seen between
the monumental resonance theory of Helmholtz , and
two-page “telephone theory” that
to it, according to which the ear is merely a telephone receiver
that transmits pulses to the brain .
Helmholtz theory . One can speculate that they disapproved
of Ohm’s choice (Sect. 2.3), objected to the
obligatory Fourier analysis, and in general resented the
weight of Helmholtz’s authority. Some of these theories
as “temporal” (e.g. those of
others were essentially variations on the theme of a resonant
in frog or rabbit nerves (352 per second) were insufficient
to carry pitch over its full range (up to 4-5 kHz
for musical pitch). The need for high firing rates was relaxed
in 1930 by Wever and Bray’s “volley theory” .
Subsequent measurements from the auditory nerve con-
firmed that the volley principle is essentially valid (in a
stochastic form), in that synchrony to temporal features
is measurable up to about 4-5 kHz in the auditory nerve
. Synchrony is also observed at more central neural
relays, but the upper frequency limit decreases as one
Temporal and resonance models differ essentially in
the time required to make a frequency measurement.
Resonance involves the build-up of energy by accumulation
of successive waves, and this requires time that
varies inversely with frequency resolution. The relation
_ _ _ _ _ _ _ that constrains temporal and spectral resolution
was formalized by G´abor , but it was known already
to Helmholtz. Helmholtz reasoned that notes occur
at a rate of up to 8 per second in music, and from this he
calculated the narrowest possible bandwidth for cochlear
filters. Frequency resolution was thus dictated by necessary
temporal resolution, rather than by constraints related
to the implementation of cochlear filters.
In contrast, a time-domain mechanism needs just
enough time to measure the interval between two events
(plus time to make sure that each event is an event, plus
time to make sure that they are not both part of a larger
pattern). The time required is on the order of two periods
of the lowest expected frequency; accuracy is limited
only by noise or imperfection of the implementation .
An explanation of the puzzling fact that a time-domain
mechanism can escape G´abor’s relation was given by
A weakness of temporal models, as described so far,
is their reliance on events. Events need of course to be
extracted from the waveform (or from the neural pattern
that it evokes). For simple waveforms this is trivial: one
may use peaks or zero-crossings. For complex waveforms
the problem is more delicate, as evident from the
difficulties encountered by time-domain methods of fundamental
frequency estimation . It is hard (perhaps
impossible) to find a definition of “event” that allows stable
period measurement in every case.
This weakness is evident in the phase sensitivity of
early temporal models . For example, a mechanism
that measures intervals between peaks is confused by
waveforms that have several peaks per period. A mechanism
that measures intervals between envelope peaks is
confused by phase manipulations that produce two envelope
peaks per period, etc. As pitch is often invariant for
such phase manipulations, such phase-sensitive mechanisms
cannot hold. The autocorrelation model provided
a solution to this problem.
In the autocorrelation (AC) model, each sample of the
waveform is used, as it were, as an “event”. Each is compared
to every other sample, and the inter-event interval
that gives the best match (on average) indicates the period.
Concretely, comparison is performed by multiplying
samples and summing the products over a time window.
If samples are equal their products tend to be large, and
so the autocorrelation function (ACF) has a peak at the
period (and its multiples). The peak is the cue to pitch. A
slightly more straightforward idea is to subtract samples
and sum the squared differences, as proposed in the cancellation
model of . The cue to pitch is then a dip in
the difference function. Cancellation and AC models are
formally equivalent .
The original formulation of the AC model is due to
Licklider , although an interesting precursor was proposed
up the basilar membrane, is reflected at the apex, travels
down, and meets the next pulse at a position that indicates
the period . In Licklider’s model, the ACF was
calculated within the auditory nervous system, for each
channel of the auditory filter bank. The model was reformulated
and implemented computationally by Meddis
and Hewitt , and confronted to autocorrelation statistics
of actual nerve recordings by Cariani and Delgutte
. A similar model based on first order interspike interval
was proposed by
was cited earlier. Another variant is the strobed temporal
integration model of Patterson and colleagues, in
which patterns are cross-correlated with a strobe function
consisting of one pulse per period . Yost proposed a
simpler predictive model based on waveform autocorrelation
. One may cite also a number of “autocorrelation”
models in which the ACF was produced by an internal
“Fourier transformer” operating on a spectral profile
coming from the cochlea .
An important theorem, the Wiener-Khintchine theorem,
say that ACF and power spectrum are Fourier transforms
one of the other. In this sense the AC model can be
seen as an incarnation of the two steps of spectral analysis
and pattern matching. This implies a relation between
these rival approaches, as stressed early on by de Boer
. They differ of course in how they might be implemented
in the auditory nervous system, in properties such
as frequency versus temporal resolution (Sect. 4), and in
the way they can be extended to handle mixtures of tones
It is interesting to compare autocorrelation to the
string which we encountered several times in this review.
Implementation of autocorrelation requires a delay, associated
with a multiplier (e.g. a coincidence-detector neuron).
Delayed patterns are multipliedwith undelayed patterns.
The string too consists of a delay that, as it were,
feeds upon itself. Delayed patterns are added to undelayed
patterns, and their sum delayed again. This shows
a basic similarity between string and AC. It also shows
their difference. In the AC model a pattern is delayed at
most once. In the string it is delayed many times, and
these multiple delays are necessary for the build-up of
resonance that allows the string to be selective.
Autocorrelation and pattern matching are the two major
options for explaining pitch today. Pitch is evoked mainly
by stimuli that are periodic, and its value depends on their
period. The two approaches can be seen as two different
ways of extracting the period fromthe stimulus. Autocorrelation
does so directly, and pattern-matching indirectly
via a first stage of Fourier transformation. The choice between
them corresponds to that made by Ohm, a century
and a half ago (Sect. 2.3).
Cochlear frequency resolution, as Helmholtz pointed
out, must be limited. Filters are of roughly constant
“Q”, and thus have difficulty resolving upper harmonics,
closely spaced on a logarithmic scale. Pattern-matching
depends on frequency resolution, and cannot work for
stimuli that contain only partials that are unresolved. Indeed,
such stimuli tend to have a weak pitch, and this can
be interpreted in favor of pattern-matching . On the
other hand, the pitch does exist and thus needs explaining,
which pattern matching cannot do. This argues in
favor of the AC model. The AC model could, in principle,
cover both resolved and unresolved stimuli, but the
marked behavioral differences between them suggest that
there might instead be two mechanisms [48, 49, 50, 51].
The superior performance in pitch tasks for conditions
in which partials are resolved is strongly suggestive of a
pattern-matching mechanism, that breaks down for unresolved
conditions. It might nevertheless be due to other
factors that co vary with “resolution” . The issue of resolved
vs. unresolved is currently a central issue in pitch
Pattern matching and AC models both have many
variants. At times discussions may tend to focus on
relatively minor differences between rival formulations
(e.g. between Terhardt’s vs. Goldstein’s formulation
of pattern-matching, or between first-order and all-order
spike statistics for the AC model). The historical approach
is useful to widen the perspective, to emphasize
the similarities between variants, and possibly even to
suggest new, perhaps radically different, directions in
which to seek explanations of pitch and hearing.
This author is mainly interested (Licklider would
have said “ego-involved” ) in a particular variant of
the AC model, cancellation. The reason is that it brings
together mechanisms of pitch and of sound segregation
that may be of use in particular to explain perception of
multiple pitch [53, 47]. An algorithm based on cancellation
has recently proved to be effective for fundamen-
tal frequency estimation . The concept of cancellation
fits well with the ideas on redundancy and neural
metabolism reduction of Barlow .
Interest in pitch is fueled by interest in music, a very
old activity. Ideas for this review were searched for in
sources as ancient and diverse as possible. There are
big gaps. Many important sources are known only indirectly
from citations of later authors, suggesting that
much material of interest has been lost. Indeed, there
is evidence that some of the knowledge that developed
over the last 25 centuries was known long before that, in
Sources consulted were exclusively in English or French.
Those written in Latin, German or other languages were
inaccessible for lack of linguistic competence. A more
complete review is due to appear shortly .
The history of models of pitch perception has been reviewed.
Modern ideas reincarnate older ideas, and their
roots extend as far back as records are available. Models
that are in competition today may have common roots.
The historical approach allows commonalities and differences
to be put in perspective. Hopefully this should help
to defuse sterile controversy that is sometimes harmful to
the progress of ideas . It also may be of use to newcomers
to the field to understand, say, why psychoacousticians
insist on studying musical pitch with unresolved
stimuli (that sound rather unmusical), why they add low
pass noise (which makes tasks even more difficult), etc.
The good reasons for these customs are easier to understand
with a vision of the debates fromwhich present-day
pitch theory evolved.
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